Hire an LLM Engineer, Dallas

Hire an LLM engineer in Dallas.

Dallas insurance companies, HUB International, Hilltop Holdings, Employers Holdings, and AT&T at its Dallas headquarters both run large-scale manual data extraction operations. Insurance: data entry clerks extract information from medical records, police reports, and repair invoices into claims systems. At $25/hour and 8 minutes per document, a team of 20 clerks costs $700,000 per year to process documents that an LLM pipeline handles at 10x the speed.

AT&T: field technicians write free-text notes on network maintenance jobs. Specific fields, asset ID, fault type, repair action, parts replaced, need to enter the network management system. Manual re-entry is the current process for most field notes. An LLM extraction pipeline reads the note and populates the structured fields automatically.

Fixed-price engagements, scoped to your data and integration needs.

How we work

Tell us which document type your team extracts manually and the fields they key in.

The data entry cost hiding in claims and operations.

$700k/yr

Cost of a 20-person data entry team at $25/hr

Processing one document type, 8 minutes per document

10x faster

LLM pipeline processing speed vs. manual extraction

500 documents per hour vs. 50 per hour for a trained clerk

95%+

Field-level extraction accuracy on well-formatted documents

Ambiguous records flagged to human review queue

What changes when you replace manual extraction.

The claims data entry team's job does not disappear, it shifts. Instead of keying every field from every document, the team reviews the LLM's extractions for flagged records. Typical flag rate on clean insurance documents: 5 to 15% of records have at least one field with confidence below threshold. Those records go to the queue. The other 85 to 95% process straight through.

For a 20-person team processing 10,000 claims per month, this means the same monthly claim volume gets processed with 3 to 4 people handling reviews rather than 20 people doing manual entry. The remaining 16 to 17 positions shift to quality review and exception handling.

The other change is error rate. Experienced data entry clerks have a transcription error rate of 1 to 3% on repetitive extraction tasks, not because they are careless, but because the human attention required for hours of repetitive work is limited. The LLM makes different errors: it may misparse an ambiguous field, but it does not transpose digits or skip fields due to fatigue.

The combined error profile of an LLM pipeline with a human review queue is typically lower than pure manual extraction, because the humans are reviewing edge cases with full attention rather than processing a high volume of routine records.

What a Dallas extraction pipeline includes.

Multi-format document intake

HL7/CCDA parsing for EHR exports, Textract for scanned documents, handwriting-aware OCR for physician notes. Format detection routes each document to the right pre-processor.

Typed extraction schema

JSON Schema matching your claims system's intake fields. Field types, required fields, and acceptable value ranges defined before any model call.

Confidence scoring

Per-field confidence scores. Extractions below threshold go to human review queue rather than writing uncertain values.

Claims examiner review queue

Review interface showing original document alongside the model's extraction and confidence scores. Examiners edit fields directly and approve the record.

Claims system integration

Output writes to your claims management system via REST API or direct database insert. Field mapping and write logic documented.

Accuracy reporting

Weekly accuracy metrics per document type and per field, based on sample spot-checks. Tracks accuracy over time as document formats change.

Questions from Dallas insurance and telecom teams.

How does the pipeline handle the variety of medical record formats a Dallas insurer receives?: Medical records arrive in multiple formats: structured EHR exports, scanned paper records, handwritten physician notes, and faxed documents. Each requires a different pre-processing step before the LLM extraction. For structured EHR exports, we parse the HL7 or CCDA format directly. For scanned and faxed documents, AWS Textract handles OCR. Handwritten notes use a specialized handwriting-aware OCR model before the LLM step. The pipeline routes each document to the appropriate pre-processing path automatically based on format detection.
Can an LLM pipeline extract structured data from AT&T field technician notes for network maintenance tracking?: Yes. Field technician notes are a classic unstructured extraction problem: the technician writes in natural language, but the downstream system needs specific fields, asset ID, fault type, repair action taken, parts replaced, time to repair, and next service date. We define the extraction schema from your current manual entry form fields, then build an LLM pipeline that reads the note and populates those fields. Notes that are too ambiguous for confident extraction go to a review queue rather than writing incomplete records.
How do you measure accuracy on insurance claims extraction to satisfy an HUB International or Hilltop Holdings compliance team?: We build a labeled test set of 300 to 500 historical claims documents with field values verified by your claims team. We measure precision and recall separately for each field type. Before go-live, you receive an accuracy report showing the extraction rate and error rate per field. For fields where accuracy falls below the threshold your compliance team requires, we either add more training examples to improve the prompt, or we set a tighter confidence threshold that routes more of those fields to human review.
What is the integration path for writing extracted claim data into a claims management system?: Most Dallas insurance claims systems support inbound data via REST API or database insert. We build the integration layer that maps extracted fields to your system's schema and handles the write operation. For systems that require human approval before committing, we add an approval queue where a claims examiner reviews the extracted record for a flagged claim before it writes. For straight-through processing on clean, high-confidence claims, the pipeline writes directly without a review step.

Ready to reduce your claims extraction headcount?

Tell us your current team size, the document types they process, and the claims system the data goes into. We will send a scope and cost estimate within one business day.

Hire an LLM engineer in Dallas.

Fixed-price engagements, scoped to your data and integration needs.

The data entry cost hiding in claims and operations.

$700k/yr

Cost of a 20-person data entry team at $25/hr

Processing one document type, 8 minutes per document

10x faster

LLM pipeline processing speed vs. manual extraction

500 documents per hour vs. 50 per hour for a trained clerk

95%+

Field-level extraction accuracy on well-formatted documents

Ambiguous records flagged to human review queue

What changes when you replace manual extraction.

What a Dallas extraction pipeline includes.

Multi-format document intake

HL7/CCDA parsing for EHR exports, Textract for scanned documents, handwriting-aware OCR for physician notes. Format detection routes each document to the right pre-processor.

Typed extraction schema

JSON Schema matching your claims system's intake fields. Field types, required fields, and acceptable value ranges defined before any model call.

Confidence scoring

Per-field confidence scores. Extractions below threshold go to human review queue rather than writing uncertain values.

Claims examiner review queue

Review interface showing original document alongside the model's extraction and confidence scores. Examiners edit fields directly and approve the record.

Claims system integration

Output writes to your claims management system via REST API or direct database insert. Field mapping and write logic documented.

Accuracy reporting

Weekly accuracy metrics per document type and per field, based on sample spot-checks. Tracks accuracy over time as document formats change.

Questions from Dallas insurance and telecom teams.

How does the pipeline handle the variety of medical record formats a Dallas insurer receives?

Medical records arrive in multiple formats: structured EHR exports, scanned paper records, handwritten physician notes, and faxed documents. Each requires a different pre-processing step before the LLM extraction. For structured EHR exports, we parse the HL7 or CCDA format directly. For scanned and faxed documents, AWS Textract handles OCR. Handwritten notes use a specialized handwriting-aware OCR model before the LLM step. The pipeline routes each document to the appropriate pre-processing path automatically based on format detection.

Can an LLM pipeline extract structured data from AT&T field technician notes for network maintenance tracking?

Yes. Field technician notes are a classic unstructured extraction problem: the technician writes in natural language, but the downstream system needs specific fields, asset ID, fault type, repair action taken, parts replaced, time to repair, and next service date. We define the extraction schema from your current manual entry form fields, then build an LLM pipeline that reads the note and populates those fields. Notes that are too ambiguous for confident extraction go to a review queue rather than writing incomplete records.

How do you measure accuracy on insurance claims extraction to satisfy an HUB International or Hilltop Holdings compliance team?

We build a labeled test set of 300 to 500 historical claims documents with field values verified by your claims team. We measure precision and recall separately for each field type. Before go-live, you receive an accuracy report showing the extraction rate and error rate per field. For fields where accuracy falls below the threshold your compliance team requires, we either add more training examples to improve the prompt, or we set a tighter confidence threshold that routes more of those fields to human review.

What is the integration path for writing extracted claim data into a claims management system?

Most Dallas insurance claims systems support inbound data via REST API or database insert. We build the integration layer that maps extracted fields to your system's schema and handles the write operation. For systems that require human approval before committing, we add an approval queue where a claims examiner reviews the extracted record for a flagged claim before it writes. For straight-through processing on clean, high-confidence claims, the pipeline writes directly without a review step.

Ready to reduce your claims extraction headcount?

Tell us your current team size, the document types they process, and the claims system the data goes into. We will send a scope and cost estimate within one business day.